Some people claim that NoSQL is premature optimization and places an extra burden on the developer. The main points seem to be that NoSQL has no schemas and drops consistency in favour of the other parts of CAP(availability, partitioning).

I largely agree, however, I think we should differentiate between two kinds of NoSQL databases.

ScaleDB: Hard-core MapReduce, thousands of nodes, sacrifices everything for speed and scalability. If you use this below Google-size, you have no idea what you’re doing.

EasyDB: Relax, life is to short to update your schema and master SQL, give me an easy API to persist and query my data and I’ll be happy. I really think some NoSQL databases should realize this and drop scalability as a prepackaged buzzword feature and focus on the real needs of the majority of their user base.

Relax, does that word ring a bell? It’s the tag line of CouchDB. With its nice REST API and with replication as the only way to scale horizontally, I think CouchDB classifies as an EasyDB.

However, CouchDB uses MVCC, this avoids locking and provides a form of consistency, but it does place the burden of handling update conflicts on the client. Or… does it?

I would like to draw a parallel with how Clojure handles controlled shared mutable state. The simplest form present in Clojure is the Atom.

An atom is a MVCC construct that provides a low-level compare-and-set! function that executes an atomic update if the expected old value matches the actual value. Much like CouchDB compares the _rev of a document before updating.

Interestingly, though, atoms also provide the very convenient swap!, which takes a pure function that takes the old value and returns the new one. swap! calls compare-and-set! in a loop, recomputing the new value on every iteration until the update succeeds.